Unsupervised Algorithm

Unsupervised algorithms aim to extract meaningful patterns and structures from unlabeled data without relying on pre-defined categories, addressing the limitations of supervised learning where labeled data is scarce or expensive to obtain. Current research focuses on developing novel algorithms and adapting existing architectures like transformers and autoencoders for various tasks, including clustering, anomaly detection, and accuracy estimation under distribution shifts. These advancements are significant for diverse applications, ranging from improving computer vision and natural language processing to enabling more robust and efficient machine learning in domains with limited labeled data, such as healthcare and materials science.